Perhaps an ironic aspect of tail events is that it is not the expected or foreseeable events (aka the known unknowns) that cause the greatest market upheavals, but rather the events from left field (the unknown unknowns). More often than not, true tail events often 1) have little or no historical precedent; and 2) are difficult to anticipate a priori. Backtesting, by contrast, is by definition a backward-looking process that optimizes “to fight the war.” As a result, hedging strategies that are designed for a specific event or asset class that have been responsible for tails in the past may be optically attractive from a backtesting perspective but may not necessarily outperform if a future tail event is greatly dissimilar to prior shocks.
Nonetheless, dynamic tail-hedge strategies in the form of algorithmic indexes can provide a liquid, transparent and easily investable solution to mitigate the impact of a “fat tail” or black swan market event. In conclusion, the volatility buffer provided by a tail hedge not only serves to reduce the downswing in overall portfolio performance, but also could allow a credit reserve to put money to work after market shock. A systematic tail hedge that also avoids a heavy cost-of-carry can keep PMs off the sidelines during the very time they should be the most active in navigating periods of market duress.
1 Algorithmic indexes are rules-based, systematic investment strategies that are created to be transparent, liquid and investable. These indexes can, in turn, be packaged into structured notes, OTC swaps and options, and even funds. Algorithmic indexes differ from “trading algorithms” which typically focus on the execution of stocks and baskets of stocks.